Temperature Prediction of Distributed Fiber Optic Systems Based on Trend Extrapolation Method
Distributed temperature sensors are widely used for temperature monitoring.Based on the background,this article proposes a distributed fiber optic system with temperature monitoring and prediction functions.While implementing temperature monitoring,a trend extrapolation model based on machine learning has been established to predict future temperatures through continuously updated historical data.The experiment simulated the temperature changes of general fires and made predictions.The results show that when the sampling time is 5 seconds and the temperature is predicted after 1 minute,the maximum prediction error is 2.88 ℃,and the MSE is 0.958 ℃,indicating that the model has high accuracy and has certain reference value for engineering practice.
optical fiber temperature measuringtrend extrapolation methodmachine learningpolynomial fitting
万方数据
维普
